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Добірка наукової літератури з теми "Remote sensing approaches"

Автор: Grafiati
Опубліковано: 10 січня 2023
Оновлено: 28 січня 2023

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Статті в журналах з теми "Remote sensing approaches":

1

Jeon, Gwanggil. "Artificial Intelligence-Based Learning Approaches for Remote Sensing." Remote Sensing 14, no. 20 (October 18, 2022): 5203. http://dx.doi.org/10.3390/rs14205203.

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2

Garaba, Shungudzemwoyo P., Daniela Voß, Jochen Wollschläger, and Oliver Zielinski. "Modern approaches to shipborne ocean color remote sensing." Applied Optics 54, no. 12 (April 14, 2015): 3602. http://dx.doi.org/10.1364/ao.54.003602.

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3

Wang, Xuan, Jinglei Yi, Jian Guo, Yongchao Song, Jun Lyu, Jindong Xu, Weiqing Yan, Jindong Zhao, Qing Cai, and Haigen Min. "A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing." Remote Sensing 14, no. 21 (October 28, 2022): 5423. http://dx.doi.org/10.3390/rs14215423.

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At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.
4

Popova, Svetlana Mikhailovna, Valentin Borisovich Uvarov, and Andrey Aleksandrovich Yanik. "Regulation of Remote Sensing of the Earth from Space: International Practice." Международное право, no. 3 (March 2022): 1–27. http://dx.doi.org/10.25136/2644-5514.2022.3.38577.

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The article is devoted to the results of the study of international experience in regulating activities in the field of remote sensing of the Earth from space. The institutional and legal approaches of a number of countries and regional associations with a developed remote sensing sector are considered. The purpose is to identify models of regulation and experience useful for russian context. The source base consisted of more than 100 official documents (normative legal acts, strategies, programs, official reports, other materials), as well as academic publications related to the issue under consideration. General scientific research methods, content analysis, formal legal analysis, and comparative legal approaches were used to solve the research tasks. Summary information (on the main regulatory legal acts and institutions regulating remote sensing, features of licensing procedures, approaches to the storage and dissemination of remote sensing data) is presented in tabular form. Authors consider the approaches of states to remote sensing regulation can be described by a limited number of core models (three legal models, two institutional approaches), but international practice differs in a wide variety of details that reflect the specifics of the national context. Authors found the essential similarity of approaches to the regulation of space activities of the two space powers – the Russian Federation and the United States, so the analysis of American failures with the privatization of remote sensing in the late 1970s and 1980s can be useful in determining the ways of development and commercialization of this sector in Russia. The relevance of attention to the international practice of remote sensing regulation is justified by the importance of creating favorable legal mode for the development of this sector in Russia facing the challenges of rapid growth of the market for active Earth observation from space, as well as sharp expansion in the number of users and applications of remote sensing data.
5

Lausch, Angela, Michael E. Schaepman, Andrew K. Skidmore, Eusebiu Catana, Lutz Bannehr, Olaf Bastian, Erik Borg, et al. "Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics." Remote Sensing 14, no. 9 (May 9, 2022): 2279. http://dx.doi.org/10.3390/rs14092279.

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Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed.
6

Abdollahi, Abolfazl, Biswajeet Pradhan, Nagesh Shukla, Subrata Chakraborty, and Abdullah Alamri. "Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review." Remote Sensing 12, no. 9 (May 2, 2020): 1444. http://dx.doi.org/10.3390/rs12091444.

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One of the most challenging research subjects in remote sensing is feature extraction, such as road features, from remote sensing images. Such an extraction influences multiple scenes, including map updating, traffic management, emergency tasks, road monitoring, and others. Therefore, a systematic review of deep learning techniques applied to common remote sensing benchmarks for road extraction is conducted in this study. The research is conducted based on four main types of deep learning methods, namely, the GANs model, deconvolutional networks, FCNs, and patch-based CNNs models. We also compare these various deep learning models applied to remote sensing datasets to show which method performs well in extracting road parts from high-resolution remote sensing images. Moreover, we describe future research directions and research gaps. Results indicate that the largest reported performance record is related to the deconvolutional nets applied to remote sensing images, and the F1 score metric of the generative adversarial network model, DenseNet method, and FCN-32 applied to UAV and Google Earth images are high: 96.08%, 95.72%, and 94.59%, respectively.
7

Lukin, V. V., S. K. Abramov, N. N. Ponomarenko, S. S. Krivenko, M. L. Uss, Benoit Vozel, Kacem Chehdi, Karen O. Egiazarian, and J. T. Astola. "APPROACHES TO AUTOMATIC DATA PROCESSING IN HYPERSPECTRAL REMOTE SENSING." Telecommunications and Radio Engineering 73, no. 13 (2014): 1125–39. http://dx.doi.org/10.1615/telecomradeng.v73.i13.10.

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8

Lin, Li, Liping Di, Chen Zhang, Liying Guo, and Yahui Di. "Remote Sensing of Urban Poverty and Gentrification." Remote Sensing 13, no. 20 (October 9, 2021): 4022. http://dx.doi.org/10.3390/rs13204022.

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In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.
9

Mehmood, Maryam, Ahsan Shahzad, Bushra Zafar, Amsa Shabbir, and Nouman Ali. "Remote Sensing Image Classification: A Comprehensive Review and Applications." Mathematical Problems in Engineering 2022 (August 2, 2022): 1–24. http://dx.doi.org/10.1155/2022/5880959.

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Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.
10

Raju, Manthena Narasimha, Kumaran Natarajan, and Chandra Sekhar Vasamsetty. "Object Recognition in Remote Sensing Images Based on Modified Backpropagation Neural Network." Traitement du Signal 38, no. 2 (April 30, 2021): 451–59. http://dx.doi.org/10.18280/ts.380224.

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In the area of remote sensing, one of the problems is how high-quality remote sensing images are automatically categorized and classified. There have been many suggestions for alternatives. Amongst these, there are drawbacks of approaches focused on low visual and intermediate visual characteristics. This article, therefore, adopts the deep learning method for classifying high-resolution remote sensing picture scenes to learn semantic knowledge. Most of the existing neural network convolution approaches are focused on the model of transfer training and there are comparatively like hidden Marco models, linear fitting methods, the creation of new neural networks based on the latest high-resolution remote sensing picture data sets. But in this paper, we used a modified backpropagation neural network is proposed to detect the objects in images. To test the performance of the proposed model we use two remote sensing data sets benchmark tests were done. The test-precision, precision, reminder, and F1 scores are all fine with the Assist data collection. The precision, precision, reminder, and F1 score are all enhanced on the SIRI-WHU dataset. The proposed system has better precision and robustness compared to the current approaches including the most conventional methods and certain profound learning methods to scene distinguish high-resolution remote sensing pictures.
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Статті в журналах Дисертації Книги

Дисертації з теми "Remote sensing approaches":

1

Bejiga, Mesay Belete. "Adversarial approaches to remote sensing image analysis." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/257100.

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The recent advance in generative modeling in particular the unsupervised learning of data distribution is attributed to the invention of models with new learning algorithms. Among the methods proposed, generative adversarial networks (GANs) have shown to be the most efficient approaches to estimate data distributions. The core idea of GANs is an adversarial training of two deep neural networks, called generator and discriminator, to learn an implicit approximation of the true data distribution. The distribution is approximated through the weights of the generator network, and interaction with the distribution is through the process of sampling. GANs have found to be useful in applications such as image-to-image translation, in-painting, and text-to-image synthesis. In this thesis, we propose to capitalize on the power of GANs for different remote sensing problems. The first problem is a new research track to the remote sensing community that aims to generate remote sensing images from text descriptions. More specifically, we focus on exploiting ancient text descriptions of geographical areas, inherited from previous civilizations, and convert them the equivalent remote sensing images. The proposed method is composed of a text encoder and an image synthesis module. The text encoder is tasked with converting a text description into a vector. To this end, we explore two encoding schemes: a multilabel encoder and a doc2vec encoder. The multilabel encoder takes into account the presence or absence of objects in the encoding process whereas the doc2vec method encodes additional information available in the text. The encoded vectors are then used as conditional information to a GAN network and guide the synthesis process. We collected satellite images and ancient text descriptions for training in order to evaluate the efficacy of the proposed method. The qualitative and quantitative results obtained suggest that the doc2vec encoder-based model yields better images in terms of the semantic agreement with the input description. In addition, we present open research areas that we believe are important to further advance this new research area. The second problem we want to address is the issue of semi-supervised domain adaptation. The goal of domain adaptation is to learn a generic classifier for multiple related problems, thereby reducing the cost of labeling. To that end, we propose two methods. The first method uses GANs in the context of image-to-image translation to adapt source domain images into target domain images and train a classifier using the adapted images. We evaluated the proposed method on two remote sensing datasets. Though we have not explored this avenue extensively due to computational challenges, the results obtained show that the proposed method is promising and worth exploring in the future. The second domain adaptation strategy borrows the adversarial property of GANs to learn a new representation space where the domain discrepancy is negligible, and the new features are discriminative enough. The method is composed of a feature extractor, class predictor, and domain classifier blocks. Contrary to the traditional methods that perform representation and classifier learning in separate stages, this method combines both into a single-stage thereby learning a new representation of the input data that is domain invariant and discriminative. After training, the classifier is used to predict both source and target domain labels. We apply this method for large-scale land cover classification and cross-sensor hyperspectral classification problems. Experimental results obtained show that the proposed method provides a performance gain of up to 40%, and thus indicates the efficacy of the method.
2

Lewis, Ryan H. "Topological & network theoretic approaches in hyperspectral remote sensing /." Online version of thesis, 2010. http://ritdml.rit.edu/handle/1850/12274.

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3

Slade, Jr Wayne Homer. "Computational Intelligence Approaches to Ocean Color Inversion." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/SladeWH2004.pdf.

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4

Johnson, Michele K. "Remote sensing and the South, a critical evaluation of common approaches." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq37557.pdf.

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5

Sumaryono, Sumaryono. "Assessing Building Vulnerability to Tsunami Hazard Using Integrative Remote Sensing and GIS Approaches." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-123909.

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6

Kearney, Sean Patrick. "Integrating field and remote sensing approaches to evaluate ecosystem services from agriculture in smallholder landscapes." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62110.

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Agriculture now covers over a third of the Earth’s terrestrial surface, and smallholder farmers alone manage over a billion hectares globally. As stewards of the land, smallholders do much more for human well-being than just harvest useful products. However, a conventionally narrow focus on productivity over the last half- century now threatens ecosystem health and long-term agricultural production, particularly as global climate change accelerates. Agroecological and ‘climate-smart’ agricultural (CSA) practices have been proposed to both mitigate climate change and build resilience by enhancing multiple ecosystem services (ES), and policies are emerging to incentivize the adoption of such practices. In order to (1) better understand how agroecological and CSA management alternatives impact multiple ES, and (2) contribute to operationalizing monitoring of ES in smallholder landscapes, I present research from El Salvador combining field methods and remote sensing analysis to evaluate multiple ES. Using data from on-farm field trials, I developed composite ES indices to demonstrate distinct benefits and synergies among multiple ES from agroforestry and, to a lesser extent, organic management (i.e., CSA) compared to conventional management. I also identified a subset of easy-to-measure field proxies that correlate well with multiple ES, and proposed an improved method to compare relative erosion resulting from different land management practices. At the landscape scale, I focused on emerging techniques to map aboveground woody biomass (AGWB) – a large terrestrial carbon sink and indicator of agroforestry management – using high-spatial-resolution satellite imagery and airborne laser scanning (ALS). I showed how satellite data could be used to quantify AGWB at the watershed to landscape scale with uncertainties of less than 5%, and suggest that a singular focus on plot-scale uncertainty limits the operationalization of satellite-based approaches to monitor AGWB. I also present a novel approach to using ALS that improves the accuracy of measuring AGWB in trees outside of forests (e.g., agroforestry, hedgerows) and apply it to show that these trees contain substantial AGWB within smallholder landscapes, further demonstrating the ES benefits of agroforestry. This dissertation contributes to designing simple and cost-effective monitoring strategies to help operationalize policies promoting management practices that enhance multiple ES in smallholder agriculture.
Land and Food Systems, Faculty of
Graduate
7

Ali, Fadi. "Urban classification by pixel and object-based approaches for very high resolution imagery." Thesis, Högskolan i Gävle, Samhällsbyggnad, GIS, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-23993.

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Recently, there is a tremendous amount of high resolution imagery that wasn’t available years ago, mainly because of the advancement of the technology in capturing such images. Most of the very high resolution (VHR) imagery comes in three bands only the red, green and blue (RGB), whereas, the importance of using such imagery in remote sensing studies has been only considered lately, despite that, there are no enough studies examining the usefulness of these imagery in urban applications. This research proposes a method to investigate high resolution imagery to analyse an urban area using UAV imagery for land use and land cover classification. Remote sensing imagery comes in various characteristics and format from different sources, most commonly from satellite and airborne platforms. Recently, unmanned aerial vehicles (UAVs) have become a very good potential source to collect geographic data with new unique properties, most important asset is the VHR of spatiotemporal data structure. UAV systems are as a promising technology that will advance not only remote sensing but GIScience as well. UAVs imagery has been gaining popularity in the last decade for various remote sensing and GIS applications in general, and particularly in image analysis and classification. One of the concerns of UAV imagery is finding an optimal approach to classify UAV imagery which is usually hard to define, because many variables are involved in the process such as the properties of the image source and purpose of the classification. The main objective of this research is evaluating land use / land cover (LULC) classification for urban areas, whereas the data of the study area consists of VHR imagery of RGB bands collected by a basic, off-shelf and simple UAV. LULC classification was conducted by pixel and object-based approaches, where supervised algorithms were used for both approaches to classify the image. In pixel-based image analysis, three different algorithms were used to create a final classified map, where one algorithm was used in the object-based image analysis. The study also tested the effectiveness of object-based approach instead of pixel-based in order to minimize the difficulty in classifying mixed pixels in VHR imagery, while identifying all possible classes in the scene and maintain the high accuracy. Both approaches were applied to a UAV image with three spectral bands (red, green and blue), in addition to a DEM layer that was added later to the image as ancillary data. Previous studies of comparing pixel-based and object-based classification approaches claims that object-based had produced better results of classes for VHR imagery. Meanwhile several trade-offs are being made when selecting a classification approach that varies from different perspectives and factors such as time cost, trial and error, and subjectivity.       Classification based on pixels was approached in this study through supervised learning algorithms, where the classification process included all necessary steps such as selecting representative training samples and creating a spectral signature file. The process in object-based classification included segmenting the UAV’s imagery and creating class rules by using feature extraction. In addition, the incorporation of hue, saturation and intensity (IHS) colour domain and Principle Component Analysis (PCA) layers were tested to evaluate the ability of such method to produce better results of classes for simple UAVs imagery. These UAVs are usually equipped with only RGB colour sensors, where combining more derived colour bands such as IHS has been proven useful in prior studies for object-based image analysis (OBIA) of UAV’s imagery, however, incorporating the IHS domain and PCA layers in this research did not provide much better classes. For the pixel-based classification approach, it was found that Maximum Likelihood algorithm performs better for VHR of UAV imagery than the other two algorithms, the Minimum Distance and Mahalanobis Distance. The difference in the overall accuracy for all algorithms in the pixel-based approach was obvious, where the values for Maximum Likelihood, Minimum Distance and Mahalanobis Distance were respectively as 86%, 80% and 76%. The Average Precision (AP) measure was calculated to compare between the pixel and object-based approaches, the result was higher in the object-based approach when applied for the buildings class, the AP measure for object-based classification was 0.9621 and 0.9152 for pixel-based classification. The results revealed that pixel-based classification is still effective and can be applicable for UAV imagery, however, the object-based classification that was done by the Nearest Neighbour algorithm has produced more appealing classes with higher accuracy. Also, it was concluded that OBIA has more power for extracting geographic information and easier integration within the GIS, whereas the result of this research is estimated to be applicable for classifying UAV’s imagery used for LULC applications.
8

Wang, Xiaozhen. "LITE aerosol retrievals with improved calibration and retrieval approaches in support of CALIPSO." Diss., The University of Arizona, 2005. http://hdl.handle.net/10150/280757.

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Two of the biggest uncertainties in understanding and predicting climate change are the effects of aerosols and clouds. NASA's satellite mission, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, will provide vertical, curtain-like images of the atmosphere on a global scale and assist scientists in better determining how aerosols and clouds affect the Earth's radiation budget. The data from a previous space shuttle mission, LITE (Lidar In-space Technology Experiment, launched in Sept., 1994), have been employed to develop algorithms (e.g., spaceborne lidar system calibration and aerosol retrievals) in support of CALIPSO. In this work, a new calibration approach for 1064 nm lidar channel has been developed via comparisons of the 532 nm and 1064 nm backscatter signals from cirrus clouds. Some modeling analyses and simulations have also been implemented for CALIPSO's narrow bandwidth receiver filter to quantitatively distinguish Cabannes scattering from the full bandwidth Rayleigh scattering and correct the calibration of 532 nm channel. LITE data were also employed in some analyses with the aim of recovering the estimates of the backscatter ratio, R, of clean air regions. The uncertainties in aerosol retrieval due to different error sources, especially the bias and random errors of the extinction-to-backscatter ratio, Sa, have been investigated. A revised Sa table look-up approach is incorporated with two notable revisions for improved S a selection, which, as a consequence enable more bounded aerosol retrievals. Approximate but quantitatively useful multiple-scattering corrections are reported using a modeled multiple scattering factor, eta, which approximates the reduced attenuation caused by multiple scattering. Assessment of multiple scattering effects for a reasonable range of eta values is included for a combination of retrieval approaches.
9

Powers, Stephanie Thompson. "Multi-scale Approaches for Evaluating the Success of Habitat Restoration in Tampa Bay, Florida." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6747.

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This research aims to further the understanding of ecological restoration success in the Tampa Bay, Florida, region. Although over four hundred restoration projects have been completed in the bay area, knowledge of their success has been hindered by the lack of assessment and transfer of information concerning project outcomes. Without comprehensive project assessment, local science will be limited in its ability to inform practice because we lack the advantage of past knowledge. Using a multi-scaled approach, a diverse set of restoration projects are evaluated, providing information on how the projects are contributing to defined targets established by the Tampa Bay Estuary Program’s guiding documents. Through execution of habitat field assessments and completion of geographic information system, remote sensing, and aerial and terrestrial laser scanning analyses, the feasibility and effectiveness of these projects is investigated. Additionally, the research provides innovative techniques for monitoring projects with relative ease, allowing project evaluation to be conducted on a more regular basis across a range of temporal and spatial scales. A cost matrix, created from this toolbox, is provided to offer land managers with a means of evaluating, regulating, and conserving restored critical coastal habitats in Tampa Bay, thus saving public dollars that may otherwise be wasted on failed projects.
10

Holman, Kiyomi. "Testing Approaches and Sensors for Satellite-Derived Bathymetry in Nunavut." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41402.

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Nearshore bathymetry in the Canadian Arctic is poorly surveyed, but is vital knowledge for coastal communities that rely on marine transportation for resources and development. Nautical charts currently available are often outdated and surveying by traditional methods is both time consuming and expensive. Satellite-derived bathymetry (SDB) offers a significantly cheaper and faster option to provide information on nearshore bathymetry. The two most common approaches to SDB are empirical and physics-based. The empirical approach is simple and typically does well when calibrated with high-quality in-situ data, whereas the physics-based approach is more difficult to implement and requires precise atmospheric correction. This project tests the practical use of five methods within the empirical and physics-based approaches to SDB, using Landsat 8 and Sentinel-2 satellite imagery, at seven sites across Nunavut. Methods tested include: the Ratio-Transform, Multiband, and Random Forest Regression methods (empirical) and radiative transfer modeling (physics-based) using two atmospheric correction models: ACOLITE and Deep Water Correction. All methods typically use geolocated water depth data for validation, as well as calibration for the empirical methods. Spectral reflectance for model inputs were collected in Cambridge Bay, NU. Water depth data were acquired from the Canadian Hydrographic Service. All processing was conducted within the framework of plugins developed for the open-source GIS software, QGIS. Results from the empirical methods were typically poor due to poor calibration data, though Random Forest Regression performed well when good calibration data were available. Due to poor quality validation data, error for the physics-based results cannot be adequately quantified in most places. Additionally, atmospheric correction remains a challenge for the physics-based methods. Overall, results indicate that where large, high-quality calibration datasets are available, Random Forest Regression performs best of all methods tested, with little bias and low mean absolute error in water less than 10 m deep. As such datasets are rare in the Arctic, the physics-based method is often the only option for SDB and is an excellent qualitative tool for informing communities of shallow bathymetry features and assessing navigation risk.
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Книги з теми "Remote sensing approaches":

1

Verdin, James P., Brian D. Wardlow, and Martha C. Anderson. Remote sensing of drought: Innovative monitoring approaches. Boca Raton, FL: CRC Press, 2012.

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2

Kavouras, Marinos. Theories of geographic concepts: Formal ontological approaches to semantic integration. Boca Raton: Taylor & Francis, 2008.

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3

1951-, Fox Jefferson, ed. People and the environment: Approaches for linking household and community surveys to remote sensing and GIS. Boston: Kluwer Academic Publishers, 2003.

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Balram, Shivanand. Open Source Approaches in Spatial Data Handling. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2008.

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5

Nussbaum, Sven. Object-based image analysis and treaty verification: New approaches in remote sensing - applied to nuclear facilities in Iran. Dordrecht: Springer, 2008.

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6

Lai, Poh C. Spatial epidemiological approaches in disease mapping and analysis. Boca Raton: Taylor & Francis, 2009.

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7

Lai, Poh C. Spatial epidemiological approaches in disease mapping and analysis. Boca Raton: Taylor & Francis, 2009.

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8

Oleschko, Klaudia. GIS in geology and earth sciences: 4th international conference, in vista of new approaches for the geoinformatics, Queretaro, Mexico 22-26 October 2007. Melville, N.Y: American Institute of Physics, 2008.

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9

R, Schott John. Remote sensing: The image chain approach. 2nd ed. New York, NY: Oxford University Press, 2007.

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10

Schott, John R. Remote sensing: The image chain approach. New York: Oxford University Press, 1997.

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Частини книг з теми "Remote sensing approaches":

1

Mulla, David J. "Satellite Remote Sensing for Precision Agriculture." In Sensing Approaches for Precision Agriculture, 19–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78431-7_2.

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2

Ustinov, Eugene A. "Three Approaches to Sensitivity Analysis of Models." In Sensitivity Analysis in Remote Sensing, 11–16. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15841-9_3.

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Nie, Liqiang, Meng Liu, and Xuemeng Song. "Conventional Image Fusion Approaches in Remote Sensing." In Image Fusion in Remote Sensing, 15–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02256-2_3.

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4

Vannan, Alastair. "Forensic Archaeological Remote Sensing and Geospatial Analysis." In Multidisciplinary Approaches to Forensic Archaeology, 19–40. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94397-8_2.

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5

Ustinov, Eugene A. "Sensitivity Analysis of Analytic Models: Linearization and Adjoint Approaches." In Sensitivity Analysis in Remote Sensing, 27–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15841-9_5.

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6

Nie, Liqiang, Meng Liu, and Xuemeng Song. "Deep Learning-Based Image Fusion Approaches in Remote Sensing." In Image Fusion in Remote Sensing, 31–54. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02256-2_4.

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7

Xia, Zong-Guo, and Keith C. Clarke. "Approaches to Scaling of Geo-Spatial Data." In Scale in Remote Sensing and GIS, 309–60. New York: Routledge, 2023. http://dx.doi.org/10.1201/9780203740170-16.

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8

Sarkar, Anasua, and Rajib Das. "Hybrid Rough-PSO Approach in Remote Sensing Imagery Analysis." In Hybrid Soft Computing Approaches, 305–27. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2544-7_10.

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9

Franklin, Janet, John Rogan, Stuart R. Phinn, and Curtis E. Woodcock. "Rationale and Conceptual Framework for Classification Approaches to Assess Forest Resources and Properties." In Remote Sensing of Forest Environments, 279–300. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0306-4_10.

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10

Lausch, Angela, Marco Heurich, Paul Magdon, Duccio Rocchini, Karsten Schulz, Jan Bumberger, and Doug J. King. "A Range of Earth Observation Techniques for Assessing Plant Diversity." In Remote Sensing of Plant Biodiversity, 309–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_13.

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AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.

Тези доповідей конференцій з теми "Remote sensing approaches":

1

Zhao, An, Lin Zheng, and Meixin Jiang. "Comparison of three sub-pixel computation approaches." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2005. http://dx.doi.org/10.1117/12.627442.

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2

Bioucas-Dias, José M., and Antonio Plaza. "Hyperspectral unmixing: geometrical, statistical, and sparse regression-based approaches." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2010. http://dx.doi.org/10.1117/12.870780.

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3

Chang, Chein-I., and Qian Du. "Noise subspace projection approaches to determination of intrinsic dimensionality of hyperspectral imagery." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373271.

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4

Özısık Baskurt, Didem, Yusuf Gür, Fatih Ömrüuzun, and Yasemin Yardımcı Çetin. "Gas detection by using transmittance estimation and segmentation approaches." In SPIE Remote Sensing, edited by Thilo Erbertseder, Thomas Esch, and Nektarios Chrysoulakis. SPIE, 2016. http://dx.doi.org/10.1117/12.2242111.

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5

Laurenzis, Martin. "Computational sensing approaches for enhanced active imaging." In Electro-Optical Remote Sensing, edited by Gary Kamerman and Ove Steinvall. SPIE, 2018. http://dx.doi.org/10.1117/12.2325566.

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6

Vohland, Michael, and Thomas Jarmer. "Comparison of different approaches to retrieve plant water content of summer barley canopies from spectroradiometric measurements." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Christopher M. U. Neale, and Ben T. Gouweleeuw. SPIE, 2006. http://dx.doi.org/10.1117/12.689689.

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7

Niethammer, U., and M. Joswig. "Analysis of UAV-based DTMs Generated by Multi-View-Stereo Approaches." In Remote Sensing 2012. Netherlands: EAGE Publications BV, 2012. http://dx.doi.org/10.3997/2214-4609.20143277.

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8

Germain, Olivier, and Philippe Refregier. "Edge detection and localization in SAR images: a comparative study of global filtering and active contour approaches." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1998. http://dx.doi.org/10.1117/12.331855.

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9

Nemmour, Hassiba, and Youcef Chibani. "Comparison between object- and pixel-level approaches for change detection in multispectral images by using neural networks." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.509934.

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10

Moreno, Jose F. "Spectral/spatial integration effects on information extraction from multispectral data: multiresolution approaches." In Satellite Remote Sensing, edited by Eric Mougin, K. Jon Ranson, and James A. Smith. SPIE, 1995. http://dx.doi.org/10.1117/12.200773.

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Звіти організацій з теми "Remote sensing approaches":

1

Friedman, Shmuel, Jon Wraith, and Dani Or. Geometrical Considerations and Interfacial Processes Affecting Electromagnetic Measurement of Soil Water Content by TDR and Remote Sensing Methods. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7580679.bard.

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Time Domain Reflectometry (TDR) and other in-situ and remote sensing dielectric methods for determining the soil water content had become standard in both research and practice in the last two decades. Limitations of existing dielectric methods in some soils, and introduction of new agricultural measurement devices or approaches based on soil dielectric properties mandate improved understanding of the relationship between the measured effective permittivity (dielectric constant) and the soil water content. Mounting evidence indicates that consideration must be given not only to the volume fractions of soil constituents, as most mixing models assume, but also to soil attributes and ambient temperature in order to reduce errors in interpreting measured effective permittivities. The major objective of the present research project was to investigate the effects of the soil geometrical attributes and interfacial processes (bound water) on the effective permittivity of the soil, and to develop a theoretical frame for improved, soil-specific effective permittivity- water content calibration curves, which are based on easily attainable soil properties. After initializing the experimental investigation of the effective permittivity - water content relationship, we realized that the first step for water content determination by the Time Domain Reflectometry (TDR) method, namely, the TDR measurement of the soil effective permittivity still requires standardization and improvement, and we also made more efforts than originally planned towards this objective. The findings of the BARD project, related to these two consequential steps involved in TDR measurement of the soil water content, are expected to improve the accuracy of soil water content determination by existing in-situ and remote sensing dielectric methods and to help evaluate new water content sensors based on soil electrical properties. A more precise water content determination is expected to result in reduced irrigation levels, a matter which is beneficial first to American and Israeli farmers, and also to hydrologists and environmentalists dealing with production and assessment of contamination hazards of this progressively more precious natural resource. The improved understanding of the way the soil geometrical attributes affect its effective permittivity is expected to contribute to our understanding and predicting capability of other, related soil transport properties such as electrical and thermal conductivity, and diffusion coefficients of solutes and gas molecules. In addition, to the originally planned research activities we also investigated other related problems and made many contributions of short and longer terms benefits. These efforts include: Developing a method and a special TDR probe for using TDR systems to determine also the soil's matric potential; Developing a methodology for utilizing the thermodielectric effect, namely, the variation of the soil's effective permittivity with temperature, to evaluate its specific surface area; Developing a simple method for characterizing particle shape by measuring the repose angle of a granular material avalanching in water; Measurements and characterization of the pore scale, saturation degree - dependent anisotropy factor for electrical and hydraulic conductivities; Studying the dielectric properties of cereal grains towards improved determination of their water content. A reliable evaluation of the soil textural attributes (e.g. the specific surface area mentioned above) and its water content is essential for intensive irrigation and fertilization processes and within extensive precision agriculture management. The findings of the present research project are expected to improve the determination of cereal grain water content by on-line dielectric methods. A precise evaluation of grain water content is essential for pricing and evaluation of drying-before-storage requirements, issues involving energy savings and commercial aspects of major economic importance to the American agriculture. The results and methodologies developed within the above mentioned side studies are expected to be beneficial to also other industrial and environmental practices requiring the water content determination and characterization of granular materials.
2

Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, December 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
3

Pyke, Benjamin J. Practical Approach To Building A Mid-Wave Remote Sensing System. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1343834.

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4

Makris, Nicholas C. A Unified Approach to Passive and Active Ocean Acoustic Waveguide Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574971.

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5

Haring, Christopher. Data collection tools for river geomorphology studies : LiDAR and traditional methods. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42502.

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The purpose of this review is to highlight LiDAR data usage for geomorphic studies and compare to other remote sensing technologies. This review further identifies survey efficiencies and issues that can be problematic in using LiDAR digital elevation models (DEMs) in completing surveys and geomorphic analysis. US Army Corps of Engineers (USACE) geospatial data collection guidance (EM 1110-1-1000) (USACE 2015) aligns with the American Society for Photogrammetry and Remote Sensing Positional Accuracy Standards for Digital Geospatial Data (ASPRS 2014). Geomorphic assessment technologies are rapidly evolving, and LiDAR data collection methods are at the forefront. The FluvialGeomorph (FG) toolbox, developed to support USACE watershed planning, is a recent example of the use of LiDAR high-resolution terrain data to provide a new, efficient approach for rapid watershed assessments (Haring et al. 2020; Haring and Biedenharn 2021). However, there are advantages and disadvantages in using LiDAR data compared to other remote sensing technologies and traditional topographic field survey methods.
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Bélanger, J. R. Prospecting in glaciated terrain: an approach based on geobotany, biogeochemistry, and remote sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1988. http://dx.doi.org/10.4095/125173.

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7

Metz, L., and A. N. Bear-Crozier. Landslide susceptibility mapping: a remote sensing based approach using QGIS 2.2 (Valmiera): technical manual. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.056.

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Mobley, Curtis D. A Spectrum-Matching and Look-Up-Table Approach to Interpretation of Hyperspectral Remote-Sensing Data. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada419452.

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Suir, Glenn, Molly Reif, and Christina Saltus. Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45241.

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Engineering With Nature (EWN®) is a US Army Corps of Engineers (USACE) Initiative and Program that promotes more sustainable practices for delivering economic, environmental, and social benefits through collaborative processes. As the number and variety of EWN® projects continue to grow and evolve, there is an increasing opportunity to improve how to quantify their benefits and communicate them to the public. Recent advancements in remote sensing technologies are significant for EWN® because they can provide project-relevant detail across a large areal extent, in which traditional survey methods may be complex due to site access limitations. These technologies encompass a suite of spatial and temporal data collection and processing techniques used to characterize Earth's surface properties and conditions that would otherwise be difficult to assess. This document aims to describe the general underpinnings and utility of remote sensing technologies and applications for use: (1) in specific phases of the EWN® project life cycle; (2) with specific EWN® project types; and (3) in the quantification and assessment of project implementation, performance, and benefits.
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Mudaliar, Saba. A Critical Study of the Radiative Transfer Approach for the Remote Sensing of Layered Random Media. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada583517.

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